from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase          =						{
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase          =						[
	    """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """AltCLIPPreTrainedModel""",
	    """AltCLIPModel""",
	    """AltCLIPTextModel""",
	    """AltCLIPVisionModel""",
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	_UpperCAmelCase          =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase          =						logging.get_logger(__name__)

_UpperCAmelCase          =						{
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   '''xlm-roberta'''
				def __init__(		self						,      lowercase=3_0_5_2_2						,      lowercase=7_6_8						,      lowercase=1_2						,      lowercase=1_2						,      lowercase=3_0_7_2						,      lowercase="gelu"						,      lowercase=0.1						,      lowercase=0.1						,      lowercase=5_1_2						,      lowercase=2						,      lowercase=0.02						,      lowercase=1E-12						,      lowercase=1						,      lowercase=0						,      lowercase=2						,      lowercase="absolute"						,      lowercase=True						,      lowercase=None						,      **lowercase						,      ):



						"""simple docstring"""





						super().__init__(pad_token_id=lowercase						,      bos_token_id=lowercase						,      eos_token_id=lowercase						,      **lowercase   )

						A_						:							Union[str, Any]      = vocab_size
						A_						:							int      = hidden_size
						A_						:							Dict      = num_hidden_layers
						A_						:							List[str]      = num_attention_heads
						A_						:							Tuple      = hidden_act
						A_						:							Optional[int]      = intermediate_size
						A_						:							Any      = hidden_dropout_prob
						A_						:							Tuple      = attention_probs_dropout_prob
						A_						:							int      = max_position_embeddings
						A_						:							Union[str, Any]      = type_vocab_size
						A_						:							List[str]      = initializer_range
						A_						:							Tuple      = layer_norm_eps
						A_						:							str      = position_embedding_type
						A_						:							Any      = use_cache
						A_						:							str      = classifier_dropout





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				@property
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						if self.task == "multiple-choice":
								A_						:							Union[str, Any]      = {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
								A_						:							str      = {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]   )






